




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=UpperCAmelCase__				)
class      snake_case	(						UpperCAmelCase__				):


   '''simple docstring'''







   UpperCamelCase__						:							str 	=   field(default="image-classification"			,	metadata={"include_in_asdict_even_if_is_default": True}				)
   UpperCamelCase__						:							ClassVar[Features] 	=   Features({"image": Image()}				)
   UpperCamelCase__						:							ClassVar[Features] 	=   Features({"labels": ClassLabel}				)
   UpperCamelCase__						:							str 	=   "image"
   UpperCamelCase__						:							str 	=   "labels"




   def 						UpperCAmelCase							(       self  :						int    ,     lowerCamelCase_  :						str      )       ->List[Any]:
     '''simple docstring'''







     if self.label_column not in features:
       raise ValueError(f'''Column {self.label_column} is not present in features.'''      )
     if not isinstance(features[self.label_column]    ,     lowerCamelCase_      ):
       raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''      )
     UpperCAmelCase__         = copy.deepcopy(self      )
     UpperCAmelCase__         = self.label_schema.copy()
     UpperCAmelCase__         = features[self.label_column]
     UpperCAmelCase__         = label_schema
     return task_template







   @property
   def 						UpperCAmelCase							(       self  :						Dict      )       ->Optional[int]:
     '''simple docstring'''







     return {
         self.image_column: "image",
         self.label_column: "labels",
     }





'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
